1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodolgy and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was c3b9cdd6ce351b95596a9ade67db014572999791.

2 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

3 Basic Exploration

Below we plot cumulative case count on a log scale by continent. Note that “Other” relates to ships.

Reported Cases by Continent

Reported Cases by Continent

Below we plot the cumulative deaths by country on a log scale:

Reported Deaths by Continent

Reported Deaths by Continent

4 Method & Assumptions

The methodology is described in detail here. We filter out countries with populations of greater than 500 000. Weeks where the deaths or cases are not greater than 50 are left out of results.

5 Results

5.1 Current \(R_{t,m}\) estimates by country

Below current (last weekly) \(R_{t,m}\) estimates are plotted on a world map.

5.1.0.1 Cases

5.1.1 Deaths

5.2 Top 10 countries

Below we show various extremes of \(R_{t,m}\) where counts (deaths or cases) exceed 50 in the last week.

5.2.1 Lowest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Belgium deaths 541 2020-12-08 0.6 0.7 0.8
Netherlands deaths 328 2020-12-08 0.7 0.8 0.9
Philippines deaths 180 2020-12-08 0.7 0.8 0.9
Algeria deaths 96 2020-12-08 0.6 0.8 0.9
Nepal deaths 106 2020-12-08 0.7 0.8 1.0
Iran deaths 2,348 2020-12-08 0.8 0.8 0.9
Iraq deaths 202 2020-12-08 0.7 0.8 0.9
Poland deaths 3,031 2020-12-08 0.8 0.8 0.9
France deaths 2,790 2020-12-08 0.8 0.8 0.9
Argentina deaths 1,158 2020-12-08 0.8 0.9 0.9

5.2.2 Lowest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Trinidad_and_Tobago cases 106 2020-12-08 0.4 0.5 0.6
Sweden cases 21,331 2020-12-08 0.6 0.6 0.6
Eswatini cases 110 2020-12-08 0.5 0.6 0.8
Poland cases 77,059 2020-12-08 0.7 0.7 0.8
Mongolia cases 87 2020-12-08 0.6 0.7 0.9
Austria cases 22,627 2020-12-08 0.7 0.7 0.8
Maldives cases 202 2020-12-08 0.6 0.7 0.9
Ecuador cases 5,559 2020-12-08 0.7 0.8 0.8
Kenya cases 4,961 2020-12-08 0.8 0.8 0.8
Angola cases 509 2020-12-08 0.7 0.8 0.9

5.2.3 Highest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Costa_Rica deaths 99 2020-12-08 1.3 1.5 1.8
Latvia deaths 66 2020-12-08 1.1 1.5 1.8
Japan deaths 243 2020-12-08 1.2 1.3 1.5
Afghanistan deaths 132 2020-12-08 1.1 1.3 1.6
Egypt deaths 140 2020-12-08 1.1 1.3 1.5
Paraguay deaths 116 2020-12-08 1.1 1.3 1.5
United_States_of_America deaths 15,698 2020-12-08 1.2 1.2 1.3
Denmark deaths 57 2020-12-08 0.9 1.2 1.5
South_Africa deaths 714 2020-12-08 1.1 1.2 1.3
Panama deaths 133 2020-12-08 1.0 1.2 1.4

5.2.4 Highest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Botswana cases 1,539 2020-12-08 2.5 5.7 10.9
Madagascar cases 172 2020-12-08 1.4 2.3 4.8
Eritrea cases 72 2020-12-08 1.4 2.0 3.1
Mauritania cases 915 2020-12-08 1.8 2.0 2.3
Yemen cases 192 2020-12-08 1.6 1.9 2.3
Burkina_Faso cases 429 2020-12-08 1.6 1.9 2.3
Somalia cases 134 2020-12-08 1.6 1.9 2.3
Senegal cases 464 2020-12-08 1.5 1.8 2.1
Haiti cases 125 2020-12-08 1.4 1.8 2.2
Niger cases 346 2020-12-08 1.5 1.7 1.9

5.3 Country Plots by Continent

Below we plot results for each country/province in a list. We filter out weeks where the upper end of confidence interval for \(R_{t,m}\) exceeds five.

5.3.1 Africa

5.3.1.1 Algeria

5.3.1.2 Angola

5.3.1.3 Benin

5.3.1.4 Botswana

5.3.1.5 Burkina_Faso

5.3.1.6 Burundi

5.3.1.7 Cameroon

5.3.1.8 Cape_Verde

5.3.1.9 Central_African_Republic

5.3.1.10 Chad

5.3.1.11 Comoros

5.3.1.12 Congo

5.3.1.13 Cote_dIvoire

5.3.1.14 Democratic_Republic_of_the_Congo

5.3.1.15 Djibouti

5.3.1.16 Egypt

5.3.1.17 Equatorial_Guinea

5.3.1.18 Eritrea

5.3.1.19 Eswatini

5.3.1.20 Ethiopia

5.3.1.21 Gabon

5.3.1.22 Gambia

5.3.1.23 Ghana

5.3.1.24 Guinea

5.3.1.25 Guinea_Bissau

5.3.1.26 Kenya

5.3.1.27 Lesotho

5.3.1.28 Liberia

5.3.1.29 Libya

5.3.1.30 Madagascar

5.3.1.31 Malawi

5.3.1.32 Mali

5.3.1.33 Mauritania

5.3.1.34 Mauritius

5.3.1.35 Morocco

5.3.1.36 Mozambique

5.3.1.37 Namibia

5.3.1.38 Niger

5.3.1.39 Nigeria

5.3.1.40 Rwanda

5.3.1.41 Senegal

5.3.1.42 Sierra_Leone

5.3.1.43 Somalia

5.3.1.44 South_Africa

5.3.1.45 South_Sudan

5.3.1.46 Sudan

5.3.1.47 Togo

5.3.1.48 Tunisia

5.3.1.49 Uganda

5.3.1.50 United_Republic_of_Tanzania

5.3.1.51 Western_Sahara

5.3.1.52 Zambia

5.3.1.53 Zimbabwe

5.3.2 America

5.3.2.1 Argentina

5.3.2.2 Bolivia

5.3.2.3 Brazil

5.3.2.4 Canada

5.3.2.5 Chile

5.3.2.6 Colombia

5.3.2.7 Costa_Rica

5.3.2.8 Cuba

5.3.2.9 Dominican_Republic

5.3.2.10 Ecuador

5.3.2.11 El_Salvador

5.3.2.12 Guatemala

5.3.2.13 Guyana

5.3.2.14 Haiti

5.3.2.15 Honduras

5.3.2.16 Jamaica

5.3.2.17 Mexico

5.3.2.18 Nicaragua

5.3.2.19 Panama

5.3.2.20 Paraguay

5.3.2.21 Peru

5.3.2.22 Puerto_Rico

5.3.2.23 Suriname

5.3.2.24 Trinidad_and_Tobago

5.3.2.25 United_States_of_America

5.3.2.26 Uruguay

5.3.2.27 Venezuela

5.3.3 Asia

5.3.3.1 Afghanistan

5.3.3.2 Bahrain

5.3.3.3 Bangladesh

5.3.3.4 Bhutan

5.3.3.5 China

5.3.3.6 India

5.3.3.7 Indonesia

5.3.3.8 Iran

5.3.3.9 Iraq

5.3.3.10 Israel

5.3.3.11 Japan

5.3.3.12 Jordan

5.3.3.13 Kazakhstan

5.3.3.14 Kuwait

5.3.3.15 Kyrgyzstan

5.3.3.16 Lebanon

5.3.3.17 Malaysia

5.3.3.18 Maldives

5.3.3.19 Mongolia

5.3.3.20 Myanmar

5.3.3.21 Nepal

5.3.3.22 Oman

5.3.3.23 Pakistan

5.3.3.24 Palestine

5.3.3.25 Philippines

5.3.3.26 Qatar

5.3.3.27 Saudi_Arabia

5.3.3.28 Singapore

5.3.3.29 South_Korea

5.3.3.30 Sri_Lanka

5.3.3.31 Syria

5.3.3.32 Taiwan

5.3.3.33 Tajikistan

5.3.3.34 Thailand

5.3.3.35 United_Arab_Emirates

5.3.3.36 Uzbekistan

5.3.3.37 Vietnam

5.3.3.38 Yemen

5.3.4 Europe

5.3.4.1 Albania

5.3.4.2 Armenia

5.3.4.3 Austria

5.3.4.4 Azerbaijan

5.3.4.5 Belarus

5.3.4.6 Belgium

5.3.4.7 Bosnia_and_Herzegovina

5.3.4.8 Bulgaria

5.3.4.9 Croatia

5.3.4.10 Cyprus

5.3.4.11 Czechia

5.3.4.12 Denmark

5.3.4.13 Estonia

5.3.4.14 Finland

5.3.4.15 France

5.3.4.16 Georgia

5.3.4.17 Germany

5.3.4.18 Greece

5.3.4.19 Hungary

5.3.4.20 Ireland

5.3.4.21 Italy

5.3.4.22 Kosovo

5.3.4.23 Latvia

5.3.4.24 Lithuania

5.3.4.25 Luxembourg

5.3.4.26 Moldova

5.3.4.27 Montenegro

5.3.4.28 Netherlands

5.3.4.29 North_Macedonia

5.3.4.30 Norway

5.3.4.31 Poland

5.3.4.32 Portugal

5.3.4.33 Romania

5.3.4.34 Russia

5.3.4.35 Serbia

5.3.4.36 Slovakia

5.3.4.37 Slovenia

5.3.4.38 Spain

5.3.4.39 Sweden

5.3.4.40 Switzerland

5.3.4.41 Turkey

5.3.4.42 Ukraine

5.3.4.43 United_Kingdom

5.3.5 Oceania

5.3.5.1 Australia

5.3.5.2 New_Zealand

5.3.5.3 Papua_New_Guinea

## Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

6 Discussion

Limitation of this method to estimate \(R_{t,m}\) are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

7 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1] A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133. [Online]. Available: https://doi.org/10.1093/aje/kwt133

[2] A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013 [Online]. Available: https://CRAN.R-project.org/package=EpiEstim

[3] European Centre for Disease Prevention and Control, “Data on the geographic distribution of COVID-19 cases worldwide.” European Union, 2020 [Online]. Available: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide